Collecting and utilizing information regarding media consumption activities and habits of audience members has long been an important goal for content distributors and creators, as well as advertisers. Having information on what programming content is being watched, as well as information on who is watching them, is often used as a starting point for determining programming ratings, and in turn, the relative values of advertisement placement during one programming content over another.
Collecting audience data included surveying a sample group comprised of randomly chosen viewers. Target audience members of the sample group would self-report their viewing history or habits over a determined period of time. Audience data gathering systems evolved with technological advancements to include set meters which were installed in viewers' homes and connected to televisions or other media consumption devices. These set meters recorded and collected the viewing habits of the viewer and transmitted them to a central database and server where the information was collected and analyzed.
Other implementations have also been developed with the advent of new media delivery channels and the increase in data collection capabilities. Particularly, some existing viewer information systems include the capability to analyze ratings information on increasingly granular levels, including daily, hourly, quarter-hourly, minute-by-minute, and even second-by-second bases. These systems may further include the ability to analyze audience gains including tune-in (turning on a television) and switch-in (changing from another channel) (collectively discussed herein as tune-in), and tune-out (audience losses, also referred to as retention) event information, which provides information regarding the time at which certain viewers tuned-in to, or tuned-out from, the particular programming.
The information provided by these known systems has been valuable in evaluating the success and appeal of certain programming content, as well as determining values for advertising time related to the programs. These and other existing data collection and analysis systems however, have been limited to providing only fundamental information on what programming content is being consumed, when it is being consumed, and in some advanced cases, who is watching. It has also failed to remove random movement from the analyzed data, where viewers who tune-in to a program purely by chance—without knowing what program they are tuning-in to—are analyzed in the same manner as another viewer who intentionally tuned-in to the program based on knowledge of the content.
For example, FIG. 1 shows a chart 100 of program ratings information provided for a particular content provider for a period of one month in September, according to an audience measurement system of the related art. This extremely broad view of the data shows only the average net impressions, or the average number of viewers that watched a given program broadcast on a given day. While this type of information may be useful in charting the overall numbers of viewers who at one point or another was exposed to each particular program, it lacks any information as to specifics of viewing patterns, viewing habits, or context to the numbers with respect to the actual content being presented during each program. Where two programs may be similar in content or presentation time, this lack of detailed information may make it difficult for content providers and creators to understand the reasons behind disparities in audience data from one program to another.
FIG. 2 shows a more detailed graph 200 providing a view of audience data during the presentation of a particular Program 1 discussed in FIG. 1 according to a more granular view of the same audience measurement system of the related art. This view graphs the number of net impressions throughout the duration of the presentation at one minute increments, including descriptions of various distinct segments of the program itself. For example, in the case of a talk show, Program 1 may be comprised of 6 content based segments, where each segment is identified by a guest of the talk show or a particular topic discussed during the talk show. Therefore in addition to a line 207 charting the progression of net impressions throughout the duration of the program, the graph 200 may include descriptors 201-206 of each segment, providing a visual correlation between a change in the net impressions and the corresponding segment presented at the time of the change.
While this type of detailed view shown in FIG. 2 provides much more information for content distributors for understanding the context behind a program's rating numbers, the graph of FIG. 2 still fails to present sufficient granularity for content distributors—and in particular, content creators—as to the true context driving the viewing numbers. Measurement of changes in net impression may also not take into context various factors for significant changes, such as lead-in (viewership due to a program that immediately precedes another program), seasonality (viewership attributable the day of the week and month of the year), and non-content related tune-out (drop in viewership attributable to a non-content related factor), thus creating skewed and unreliable data. For content distributors, and particularly for content creators, the lack of context provided by existing audience measurement systems, even when charted on a minute-by-minute basis, reduces the information's value and prevents confident action in distributing or creating content with the purpose of improving the content and generating high viewership ratings.
Thus, the existing systems and methods have been unable to provide meaningful information as to the context surrounding the audience data provided, and therefore the existing systems have thus far been limited in value to entities such as content creators or content providers for gaining a detailed and reliable understanding of viewer behavior. Therefore, it may be advantageous for a system to present actionable program performance information which includes data related to a correlation between the content being consumed and components of the audience data collected.